From 2d3cd8276ea33234b93797e1f30e9aa73629d075 Mon Sep 17 00:00:00 2001 From: jiangjinsheng Date: Thu, 21 May 2020 10:08:23 +0800 Subject: [PATCH] vm for erfc --- mindspore/ops/_grad/grad_math_ops.py | 17 +++++++++++ mindspore/ops/_op_impl/tbe/__init__.py | 1 + mindspore/ops/_op_impl/tbe/erfc.py | 39 ++++++++++++++++++++++++++ mindspore/ops/operations/__init__.py | 2 +- mindspore/ops/operations/math_ops.py | 30 ++++++++++++++++++++ tests/ut/python/ops/test_math_ops.py | 14 +++++++++ 6 files changed, 102 insertions(+), 1 deletion(-) create mode 100644 mindspore/ops/_op_impl/tbe/erfc.py diff --git a/mindspore/ops/_grad/grad_math_ops.py b/mindspore/ops/_grad/grad_math_ops.py index d4516cb80c7..f457148d513 100755 --- a/mindspore/ops/_grad/grad_math_ops.py +++ b/mindspore/ops/_grad/grad_math_ops.py @@ -361,6 +361,23 @@ def get_bprop_erf(self): return bprop +@bprop_getters.register(P.Erfc) +def get_bprop_erfc(self): + """Grad definition for `Erfc` operation.""" + exp = P.Exp() + square = P.Square() + sqrt = P.Sqrt() + cast = P.Cast() + dtype = P.DType() + + def bprop(x, out, dout): + half_root_pi = cast(2 / sqrt(F.scalar_to_tensor(np.pi)), dtype(x)) + x_square = square(x) + dx = dout * (-half_root_pi * exp(-x_square)) + return (dx,) + return bprop + + @bprop_getters.register(P.Pow) def get_bprop_pow(self): """Grad definition for `Pow` operation.""" diff --git a/mindspore/ops/_op_impl/tbe/__init__.py b/mindspore/ops/_op_impl/tbe/__init__.py index fae47af1cb4..b30eda03ea2 100644 --- a/mindspore/ops/_op_impl/tbe/__init__.py +++ b/mindspore/ops/_op_impl/tbe/__init__.py @@ -152,6 +152,7 @@ from .fused_mul_add_n import _fused_mul_add_n_tbe from .fused_mul_apply_momentum import _fused_mul_apply_momentum_tbe from .fill import _fill_op_tbe from .erf import _erf_op_tbe +from .erfc import _erfc_op_tbe from .depthwise_conv2d import _depthwise_conv2d_tbe from .depthwise_conv2d_backprop_filter import _depthwise_conv2d_backprop_filter_tbe from .depthwise_conv2d_backprop_input import _depthwise_conv2d_backprop_input_tbe diff --git a/mindspore/ops/_op_impl/tbe/erfc.py b/mindspore/ops/_op_impl/tbe/erfc.py new file mode 100644 index 00000000000..7e1b76649aa --- /dev/null +++ b/mindspore/ops/_op_impl/tbe/erfc.py @@ -0,0 +1,39 @@ +# Copyright 2020 Huawei Technologies Co., Ltd +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================ + +"""Erfc op""" +from mindspore.ops.op_info_register import op_info_register, TBERegOp, DataType + +erfc_op_info = TBERegOp("Erfc") \ + .fusion_type("ELEMWISE") \ + .async_flag(False) \ + .binfile_name("erfc.so") \ + .compute_cost(10) \ + .kernel_name("erfc") \ + .partial_flag(True) \ + .op_pattern("formatAgnostic") \ + .input(0, "x", False, "required", "all") \ + .output(0, "y", False, "required", "all") \ + .dtype_format(DataType.F16_5HD, DataType.F16_5HD) \ + .dtype_format(DataType.F16_Default, DataType.F16_Default) \ + .dtype_format(DataType.F32_5HD, DataType.F32_5HD) \ + .dtype_format(DataType.F32_Default, DataType.F32_Default) \ + .get_op_info() + + +@op_info_register(erfc_op_info) +def _erfc_op_tbe(): + """Erfc TBE register""" + return diff --git a/mindspore/ops/operations/__init__.py b/mindspore/ops/operations/__init__.py index e3e59cdfcbc..15b93e6858f 100644 --- a/mindspore/ops/operations/__init__.py +++ b/mindspore/ops/operations/__init__.py @@ -39,7 +39,7 @@ from .control_ops import ControlDepend, GeSwitch, Merge from .inner_ops import ScalarCast from .math_ops import (Abs, ACos, AddN, AssignAdd, AssignSub, Atan2, BatchMatMul, ReduceMax, ReduceMin, ReduceMean, ReduceSum, ReduceAll, ReduceProd, CumProd, - Cos, Div, Equal, EqualCount, Exp, Erf, Floor, FloorDiv, FloorMod, Acosh, + Cos, Div, Equal, EqualCount, Exp, Erf, Erfc, Floor, FloorDiv, FloorMod, Acosh, Greater, GreaterEqual, Less, LessEqual, Log, Log1p, LogicalAnd, LogicalNot, LogicalOr, MatMul, Maximum, Minimum, Mul, Neg, NMSWithMask, NotEqual, diff --git a/mindspore/ops/operations/math_ops.py b/mindspore/ops/operations/math_ops.py index 8cb597a9a3d..d8dc6c5c221 100644 --- a/mindspore/ops/operations/math_ops.py +++ b/mindspore/ops/operations/math_ops.py @@ -1067,6 +1067,36 @@ class Erf(PrimitiveWithInfer): return x_type +class Erfc(PrimitiveWithInfer): + r""" + Computes the complementary error function of `input_x` element-wise. + + Inputs: + - **input_x** (Tensor) - The input tensor. + + Outputs: + Tensor, has the same shape and dtype as the `input_x`. + + Examples: + >>> input_x = Tensor(np.array([-1, 0, 1, 2, 3]), mindspore.float32) + >>> erfc = P.Erfc() + >>> erfc(input_x) + [1.8427168, 0., 0.1572832, 0.00469124, 0.00002235] + """ + + @prim_attr_register + def __init__(self): + """init Erfc""" + self.init_prim_io_names(inputs=['x'], outputs=['y']) + + def infer_shape(self, x_shape): + return x_shape + + def infer_dtype(self, x_type): + validator.check_tensor_type_same({"x": x_type}, [mstype.float16, mstype.float32], self.name) + return x_type + + class Minimum(_MathBinaryOp): """ Computes the element-wise minimum of input tensors. diff --git a/tests/ut/python/ops/test_math_ops.py b/tests/ut/python/ops/test_math_ops.py index 33ae04486ef..6f500c45f77 100755 --- a/tests/ut/python/ops/test_math_ops.py +++ b/tests/ut/python/ops/test_math_ops.py @@ -372,6 +372,15 @@ class Log1pNet(nn.Cell): return self.log1p(x) +class ErfcNet(nn.Cell): + def __init__(self): + super(ErfcNet, self).__init__() + self.erfc = P.Erfc() + + def construct(self, x): + return self.erfc(x) + + test_case_math_ops = [ ('MatMulGrad', { 'block': GradWrap(NetWithLoss(MatMulNet())), @@ -422,6 +431,11 @@ test_case_math_ops = [ 'desc_inputs': [Tensor(np.array([[1.0, 2.0, 4.0]], np.float32))], 'desc_bprop': [Tensor(np.array([[1.0, 2.0, 4.0]], np.float32))], 'skip': ['backward']}), + ('Erfc', { + 'block': ErfcNet(), + 'desc_inputs': [Tensor(np.array([[1.0, 2.0, 4.0]], np.float32))], + 'desc_bprop': [Tensor(np.array([[1.0, 2.0, 4.0]], np.float32))], + }), ] test_case_lists = [test_case_math_ops]